Detection rules › Splunk

Unusual Number of Remote Endpoint Authentication Events

Author
Mauricio Velazco, Splunk
Source
upstream

The following analytic identifies an unusual number of remote authentication attempts from a single source by leveraging Windows Event ID 4624, which logs successful account logons. It uses statistical analysis, specifically the 3-sigma rule, to detect deviations from normal behavior. This activity is significant for a SOC as it may indicate lateral movement, malware staging, or reconnaissance. If confirmed malicious, this behavior could allow an attacker to move laterally within the network, escalate privileges, or gather information for further attacks.

MITRE ATT&CK coverage

TacticTechniques
Initial AccessT1078 Valid Accounts
PersistenceT1078 Valid Accounts
Privilege EscalationT1078 Valid Accounts
Defense EvasionT1078 Valid Accounts

Event coverage

ProviderEvent IDTitle
Security-Auditing4624An account was successfully logged on.

Stages and Predicates

Stage 1: search

search Account_Name!="*$" EventCode=4624 Logon_Type=3

Stage 2: eval

eval ... using (Account_Name)

Stage 3: bucket

bucket span=2m _time

Stage 4: stats

stats dc(ComputerName) AS unique_targets,AS target_hosts BY _time, Source_Network_Address, Source_Account

Stage 5: eventstats

eventstats avg(unique_targets) AS comp_avg BY Source_Network_Address, Source_Account

Stage 6: eval

eval ... using (comp_avg, comp_std)

Stage 7: eval

eval ... using (unique_targets, upperBound)

Stage 8: search

search `macro`

Indicators

Each row is a field, operator, and value that the rule matches. The corpus column counts how many other rules in the catalog look for the same combination: high numbers point to widely-used, community-vetted indicators. Blank or 1 shows that the indicator is specific to this rule.

FieldKindValues
Account_Namene
  • "*$" corpus 7 (splunk 7)
EventCodeeq
  • 4624 corpus 6 (splunk 6)
Logon_Typeeq
  • 3 corpus 12 (splunk 7, sigma 5)

Neighbors

Stricter alternatives (narrower than this rule)

The rules below may be useful if you find the current rule is too noisy / lacks specificity.

Broader alternatives (more inclusive than this rule)

These rules match a superset of what this rule catches. They cover the same events plus more. Use them if you want wider coverage and can absorb more false positives.